2009 · 18 citations · 10 references
ManagementCloud ComputingCloud Performance OptimizationEnergy ManagementSchedulingEnergy EfficiencyEnergy UsePerformance OptimizationResource OptimizationResource SchedulingTask SchedulingEnergy ConsumptionEnergy-aware OptimizationEnergy-aware PlacementHeuristicsPlacementPlacement HeuristicsServer ConsolidationTask Placement
This paper deals with the reduction of energy consumption in large scale systems, especially by taking into account the impact of energy consumption for server consolidation. Decreasing the number of physical hosts used while ensuring a certain level of quality of services is the goal of our approach. We introduce a metric called energetic yield which represents the quality of a task placement on a subset of machines, while taking into account quality of service and energy efficiency aspects. It measures the difference between resources required by a job and what the system allocates ultimately, while trying to save energy. Our work aims at minimizing this difference. We propose placement heuristics that are compared to the optimal solution and to a related system. In this paper, we present a set of experiments showing the relevance of this metric in order to reduce significantly energy consumption.
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